Publications by authors named "Swaminathan Kandaswamy"

8 Publications

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Evaluation of a Clinical Decision Support Strategy to Increase Seasonal Influenza Vaccination Among Hospitalized Children Before Inpatient Discharge.

JAMA Netw Open 2021 07 1;4(7):e2117809. Epub 2021 Jul 1.

Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia.

Importance: Hospitalized children are at increased risk of influenza-related complications, yet influenza vaccine coverage remains low among this group. Evidence-based strategies about vaccination of vulnerable children during all health care visits are especially important during the COVID-19 pandemic.

Objective: To design and evaluate a clinical decision support (CDS) strategy to increase the proportion of eligible hospitalized children who receive a seasonal influenza vaccine prior to inpatient discharge.

Design, Setting, And Participants: This quality improvement study was conducted among children eligible for the seasonal influenza vaccine who were hospitalized in a tertiary pediatric health system providing care to more than half a million patients annually in 3 hospitals. The study used a sequential crossover design from control to intervention and compared hospitalizations in the intervention group (2019-2020 season with the use of an intervention order set) with concurrent controls (2019-2020 season without use of an intervention order set) and historical controls (2018-2019 season with use of an order set that underwent intervention during the 2019-2020 season).

Interventions: A CDS intervention was developed through a user-centered design process, including (1) placing a default influenza vaccine order into admission order sets for eligible patients, (2) a script to offer the vaccine using a presumptive strategy, and (3) just-in-time education for clinicians addressing vaccine eligibility in the influenza order group with links to further reference material. The intervention was rolled out in a stepwise fashion during the 2019-2020 influenza season.

Main Outcomes And Measures: Proportion of eligible hospitalizations in which 1 or more influenza vaccines were administered prior to discharge.

Results: Among 17 740 hospitalizations (9295 boys [52%]), the mean (SD) age was 8.0 (6.0) years, and the patients were predominantly Black (n = 8943 [50%]) or White (n = 7559 [43%]) and mostly had public insurance (n = 11 274 [64%]). There were 10 997 hospitalizations eligible for the influenza vaccine in the 2019-2020 season. Of these, 5449 (50%) were in the intervention group, and 5548 (50%) were concurrent controls. There were 6743 eligible hospitalizations in 2018-2019 that served as historical controls. Vaccine administration rates were 31% (n = 1676) in the intervention group, 19% (n = 1051) in concurrent controls, and 14% (n = 912) in historical controls (P < .001). In adjusted analyses, the odds of receiving the influenza vaccine were 3.25 (95% CI, 2.94-3.59) times higher in the intervention group and 1.28 (95% CI, 1.15-1.42) times higher in concurrent controls than in historical controls.

Conclusions And Relevance: This quality improvement study suggests that user-centered CDS may be associated with significantly improved influenza vaccination rates among hospitalized children. Stepwise implementation of CDS interventions was a practical method that was used to increase quality improvement rigor through comparison with historical and concurrent controls.
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http://dx.doi.org/10.1001/jamanetworkopen.2021.17809DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8299313PMC
July 2021

Clinician Perceptions on the Use of Free-Text Communication Orders.

Appl Clin Inform 2021 05 2;12(3):484-494. Epub 2021 Jun 2.

MedStar Health National Center for Human Factors in Healthcare, MedStar Health Research Institute, Washington, District of Columbia, United States.

Objective: The aim of this study was to investigate (1) why ordering clinicians use free-text orders to communicate medication information; (2) what risks physicians and nurses perceive when free-text orders are used for communicating medication information; and (3) how electronic health records (EHRs) could be improved to encourage the safe communication of medication information.

Methods: We performed semi-structured, scenario-based interviews with eight physicians and eight nurses. Interview responses were analyzed and grouped into common themes.

Results: Participants described eight reasons why clinicians use free-text medication orders, five risks relating to the use of free-text medication orders, and five recommendations for improving EHR medication-related communication. Poor usability, including reduced efficiency and limited functionality associated with structured order entry, was the primary reason clinicians used free-text orders to communicate medication information. Common risks to using free-text orders for medication communication included the increased likelihood of missing orders and the increased workload on nurses responsible for executing orders.

Discussion: Clinicians' use of free-text orders is primarily due to limitations in the current structured order entry design. To encourage the safe communication of medication information between clinicians, the EHR's structured order entry must be redesigned to support clinicians' cognitive and workflow needs that are currently being addressed via the use of free-text orders.

Conclusion: Clinicians' use of free-text orders as a workaround to insufficient structured order entry can create unintended patient safety risks. Thoughtful solutions designed to address these workarounds can improve the medication ordering process and the subsequent medication administration process.
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http://dx.doi.org/10.1055/s-0041-1731002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8172259PMC
May 2021

Predicting presumed serious infection among hospitalized children on central venous lines with machine learning.

Comput Biol Med 2021 05 20;132:104289. Epub 2021 Feb 20.

Department of Biomedical Informatics, Emory School of Medicine, Atlanta, GA, USA; Department of Biomedical Engineering, Georgia Institute of Technology and Emory School of Medicine, Atlanta, GA, USA.

Background: Presumed serious infection (PSI) is defined as a blood culture drawn and new antibiotic course of at least 4 days among pediatric patients with Central Venous Lines (CVLs). Early PSI prediction and use of medical interventions can prevent adverse outcomes and improve the quality of care.

Methods: Clinical features including demographics, laboratory results, vital signs, characteristics of the CVLs and medications used were extracted retrospectively from electronic medical records. Data were aggregated across all hospitals within a single pediatric health system and used to train machine learning models (XGBoost and ElasticNet) to predict the occurrence of PSI 8 h prior to clinical suspicion. Prediction for PSI was benchmarked against PRISM-III.

Results: Our model achieved an area under the receiver operating characteristic curve of 0.84 (95% CI = [0.82, 0.85]), sensitivity of 0.73 [0.69, 0.74], and positive predictive value (PPV) of 0.36 [0.34, 0.36]. The PRISM-III conversely achieved a lower sensitivity of 0.19 [0.16, 0.22] and PPV of 0.30 [0.26, 0.34] at a cut-off of ≥ 10. The features with the most impact on the PSI prediction were maximum diastolic blood pressure prior to PSI prediction (mean SHAP = 3.4), height (mean SHAP = 3.2), and maximum temperature prior to PSI prediction (mean SHAP = 2.6).

Conclusion: A machine learning model using common features in the electronic medical records can predict the onset of serious infections in children with central venous lines at least 8 h prior to when a clinical team drew a blood culture.
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http://dx.doi.org/10.1016/j.compbiomed.2021.104289DOI Listing
May 2021

Communication through the electronic health record: frequency and implications of free text orders.

JAMIA Open 2020 Jul 6;3(2):154-159. Epub 2020 Jul 6.

Department of Mechanical and Industrial Engineering, University of Massachusetts Amherst, Amherst, Massachusetts, USA.

Communication for non-medication order (CNMO) is a type of free text communication order providers use for asynchronous communication about patient care. The objective of this study was to understand the extent to which non-medication orders are being used for medication-related communication. We analyzed a sample of 26 524 CNMOs placed in 6 hospitals. A total of 42% of non-medication orders contained medication information. There was large variation in the usage of CNMOs across hospitals, provider settings, and provider types. The use of CNMOs for communicating medication-related information may result in delayed or missed medications, receiving medications that should have been discontinued, or important clinical decision being made based on inaccurate information. Future studies should quantify the implications of these data entry patterns on actual medication error rates and resultant safety issues.
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http://dx.doi.org/10.1093/jamiaopen/ooaa020DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7382628PMC
July 2020

Electronic Progress Note Reading Patterns: An Eye Tracking Analysis.

Stud Health Technol Inform 2019 Aug;264:1684-1685

Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA.

This study used eye-tracking to understand how the order of note sections influences the way physicians read electronic progress notes. Participants (n = 7) wore an eye-tracking device while reviewing progress notes for four patient cases and then provided a verbal summary. We reviewed and analyzed verbal summaries and eye tracking recordings. Wide variation in reading behaviors existed. There was no relationship between time spent reading a section and section origin of verbal summaries.
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http://dx.doi.org/10.3233/SHTI190596DOI Listing
August 2019

Evaluating visual analytics for health informatics applications: a systematic review from the American Medical Informatics Association Visual Analytics Working Group Task Force on Evaluation.

J Am Med Inform Assoc 2019 04;26(4):314-323

Carolina Health Informatics Program, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.

Objective: This article reports results from a systematic literature review related to the evaluation of data visualizations and visual analytics technologies within the health informatics domain. The review aims to (1) characterize the variety of evaluation methods used within the health informatics community and (2) identify best practices.

Methods: A systematic literature review was conducted following PRISMA guidelines. PubMed searches were conducted in February 2017 using search terms representing key concepts of interest: health care settings, visualization, and evaluation. References were also screened for eligibility. Data were extracted from included studies and analyzed using a PICOS framework: Participants, Interventions, Comparators, Outcomes, and Study Design.

Results: After screening, 76 publications met the review criteria. Publications varied across all PICOS dimensions. The most common audience was healthcare providers (n = 43), and the most common data gathering methods were direct observation (n = 30) and surveys (n = 27). About half of the publications focused on static, concentrated views of data with visuals (n = 36). Evaluations were heterogeneous regarding setting and measurements used.

Discussion: When evaluating data visualizations and visual analytics technologies, a variety of approaches have been used. Usability measures were used most often in early (prototype) implementations, whereas clinical outcomes were most common in evaluations of operationally-deployed systems. These findings suggest opportunities for both (1) expanding evaluation practices, and (2) innovation with respect to evaluation methods for data visualizations and visual analytics technologies across health settings.

Conclusion: Evaluation approaches are varied. New studies should adopt commonly reported metrics, context-appropriate study designs, and phased evaluation strategies.
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http://dx.doi.org/10.1093/jamia/ocy190DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7647177PMC
April 2019

Designing a wrist-worn sensor to improve medication adherence: accommodating diverse user behaviors and technology preferences.

JAMIA Open 2018 Oct 27;1(2):153-158. Epub 2018 Aug 27.

Center for Research and Innovation, Swedish Medical Center, Seattle, Washington, USA.

Objectives: High medication adherence is important for HIV suppression (antiretroviral therapy) and pre-exposure prophylaxis efficacy. We are developing sensor-based technologies to detect pill-taking gestures, trigger reminders, and generate adherence reports.

Materials And Methods: We collected interview, observation, and questionnaire data from individuals with and at-risk for HIV ( = 17). We assessed their medication-taking practices and physical actions, and feedback on our initial design.

Results: While participants displayed diverse medication taking practices and physical actions, most (67%) wanted to use the system to receive real-time and summative feedback, and most (69%) wanted to share data with their physicians. Participants preferred reminders via the wrist-worn device or mobile app, and summative feedback via mobile app or email.

Discussion: Adoption of these systems is promising if designs accommodate diverse behaviors and preferences.

Conclusion: Our findings may help improve the accuracy and adoption of the system by accounting for user behaviors, physical actions, and preferences.
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http://dx.doi.org/10.1093/jamiaopen/ooy035DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6241509PMC
October 2018

The Radiologist's Gaze: Mapping Three-Dimensional Visual Search in Computed Tomography of the Abdomen and Pelvis.

J Digit Imaging 2019 04;32(2):234-240

MedStar Georgetown University Hospital, 3800 Reservoir Road NW, Washington, DC, 20007, USA.

A radiologist's search pattern can directly influence patient management. A missed finding is a missed opportunity for intervention. Multiple studies have attempted to describe and quantify search patterns but have mainly focused on chest radiographs and chest CTs. Here, we describe and quantify the visual search patterns of 17 radiologists as they scroll through 6 CTs of the abdomen and pelvis. Search pattern tracings varied among individuals and remained relatively consistent per individual between cases. Attendings and trainees had similar eye metric statistics with respect to time to first fixation (TTFF), number of fixations in the region of interest (ROI), fixation duration in ROI, mean saccadic amplitude, or total number of fixations. Attendings had fewer numbers of fixations per second versus trainees (p < 0.001), suggesting efficiency due to expertise. In those cases that were accurately interpreted, TTFF was shorter (p = 0.04), the number of fixations per second and number of fixations in ROI were higher (p = 0.04, p = 0.02, respectively), and fixation duration in ROI was increased (p = 0.02). We subsequently categorized radiologists as "scanners" or "drillers" by both qualitative and quantitative methods and found no differences in accuracy with most radiologists being categorized as "drillers." This study describes visual search patterns of radiologists in interpretation of CTs of the abdomen and pelvis to better approach future endeavors in determining the effects of manipulations such as fatigue, interruptions, and computer-aided detection.
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http://dx.doi.org/10.1007/s10278-018-0121-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6456635PMC
April 2019
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